Nicaragua

Four years ago, Juan Angel Sandoval, a resident of Barrio Buenos Aires in the Honduran municipality of Siguatepeque, received water at home only three times a week. His was not an isolated reality. Most of his neighbors, were in the same situation. "It was annoying because the water was not enough," says Juan Angel.

Many insights from behavioral science apply directly towards better understanding and addressing inequalities between men and women, in education and health, ownership of assets, access to more and better jobs, and the capacity to act on one’s own behalf and interests.

Here are three insights that stand out as critical to closing these inequalities by 2030.

Nicaragua’s Public-Private Partnerships (PPP) program is taking off. In less than a year, the country has moved quickly, overcoming hurdles to produce a PPP law, supporting regulations, and a well-staffed PPP unit. Its first deals are getting closer to fruition—the World Bank Group (WBG) team working on PPPs in Central America has just received four pre-feasibility studies for its top projects. Two of these are moving fresh out of the pipeline—the Pacific coastal toll road and a cruise ship terminal and marina in San Juan del Sur.

It might be surprising, but the majority of Central American households receive electricity subsidies, benefiting up to 8 out of 10 households in some cases. Without a doubt, this provides many poor and low-income families with access to affordable electricity.

Some months ago, during a visit to one of the Central American countries, while we were on a call with the head of the electricity dispatch center, we noticed by the tone of his voice, that he was becoming nervous. Shortly after, background voices could be heard on the line. They were experiencing a crisis and he quickly asked to continue our conversation at another time.

In the previous blog, we discussed how remote sensing techniques could be used to map and inform policymaking in secondary cities, with a practical application in 10 Central American cities. In this post, we dive deeper into the caveats and considerations when replicating these data and methods in their cities.

Can we rely only on satellite? How accurate are these results?

It is standard practice in classification studies (particularly academic ones) to assess accuracy from behind a computer. Analysts traditionally pick a random selection of points and visually inspect the classified output with the raw imagery. However, these maps are meant to be left in the hands of local governments, and not published in academic journals.

So, it’s important to learn how well the resulting maps reflect the reality on the ground.

Having used the algorithm to classify land cover in 10 secondary cities in Central America, we were determined to learn if the buildings identified by the algorithm were in fact ‘industrial’ or ‘residential’. So the team packed their bags for San Isidro, Costa Rica and Santa Ana, El Salvador.

Upon arrival, each city was divided up into 100x100 meter blocks. Focusing primarily on the built-up environment, roughly 50 of those blocks were picked for validation. The image below shows the city of San Isidro with a 2km buffer circling around its central business district. The black boxes represent the validation sites the team visited.

Land Cover validation: A sample of 100m blocks that were picked to visit in San Isidro, Costa Rica. At each site, the semi-automated land cover classification map was compared to what the team observed on the ground using laptops and the Waypoint mobile app (available for Android and iOS).

The buzz around satellite imagery over the past few years has grown increasingly loud. Google Earth, drones, and microsatellites have grabbed headlines and slashed price tags. Urban planners are increasingly turning to remotely sensed data to better understand their city.

But just because we now have access to a wealth of high resolution images of a city does not mean we suddenly have insight into how that city functions.

In an effort a few years ago to map slums, the World Bank adopted an algorithm to create land cover classification layers in large African cities using very high resolution imagery (50cm). Building on the results and lessons learned, the team saw an opportunity in applying these methods to secondary cities in Latin America & the Caribbean (LAC), where data availability challenges were deep and urbanization pressures large. Several Latin American countries including Argentina, Bolivia, Costa Rica, El Salvador, Guatemala, Honduras, Nicaragua, and Panama were faced with questions about the internal structure of secondary cities and had no data on hand to answer such questions.

A limited budget and a tight timeline pushed the team to assess the possibility of using lower resolution images compared to those that had been used for large African cities. Hence, the team embarked in the project to better understand the spatial layout of secondary cities by purchasing 1.5 meter SPOT6/7 imagery and using a semi-automated classification approach to determine what types of land cover could be successfully detected.

Originally developed by Graesser et al 2012 this approach trains (open source) algorithm to leverage both the spectral and texture elements of an image to identify such things as industrial parks, tightly packed small rooftops, vegetation, bare soil etc.

What do the maps look like? The figure below shows the results of a classification in Chinandega, Nicaragua. On the left hand side is the raw imagery and the resulting land cover map (i.e. classified layer) on the right. The land highlighted by purple shows the commercial and industrial buildings, while neighborhoods composed of smaller, possibly lower quality houses are shown in red, and neighborhoods with slightly larger more organized houses have been colored yellow. Lastly, vegetation is shown as green; bare soil, beige; and roads, gray.

In his “The People of the Abyss,” novelist Jack London describes in grim detail a devastating storm that rocked London in the early 20th century. Residents suffered terribly—some losing as much as £10,000, a ruinous sum in 1902—but none lost more than the city’s poorest.

Natural disasters are devastating to all affected; however, not everyone experiences them the same way. A dollar in losses does not mean to a rich person what it does a poor person, who may live at subsistence level or lack the means to rebound and rebuild after disaster strikes. Be it a drought or flood, the poor are always hit harder than their wealthier counterparts.

Not long after I joined the World Bank, I worked on a team assessing the extent and severity of land degradation in El Salvador. As part of this work, I went to visit the site of a soil conservation project that had been implemented a few years earlier and was considered extremely successful. Indeed, the project’s implementation report was full of numbers on linear kilometers of terraces built, and other indicators of success. Once we reached the project site, however, we looked in vain for any sign of a terrace. The terraces had once been there (there were photographs to prove it), but a few years later they no longer were.

That results may not last once a project ends is a constant concern. The extent to which it is actually a problem is hard to assess, however, as there rarely is any monitoring after a project closes.

Globally 2.9 million people died from household air pollution in 2015, caused by cooking over foul, smoky fires from solid fuels such as wood, charcoal, coal, animal dung, and agricultural crop residues. Well over 99% of these deaths were in developing countries, making household air pollution one of their leading health risk factors.

Many women across the world spend their days and evenings cooking with these fuels. They know the fumes are sickening, which is why some cook in a separate outhouse or send the children to play while they cook. Sadly, these small actions cannot fully protect the young. As for the women themselves, they suffer incredible morbidity and mortality from household air pollution.